Inequality on the frontline: A multi-country study on gender differences in mental health among healthcare workers during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Purpose Healthcare workers (HCWs) were at increased risk for mental health problems during the COVID-19 pandemic, with data from previous crises suggesting women may be particularly vulnerable. The objective of the study was to examine individual and social factors that may be associated with gender differences in psychological distress and depressive symptoms among HCWs during the initial COVID-19 pandemic outbreak and to examine the consistency of these differences across a diverse range of countries. Methods Data were collected in a cross-sectional design between March 2020 and February 2021 as part of the COVID-19 HEalth caRe wOrkErS (HEROES) study. 32,410 HCWs recruited across 22 countries completed the General Health Questionnaire-12 (GHQ-12), the Patient Health Questionnaire-9 (PHQ-9), and questions about pandemic-relevant exposures. Results Consistently across countries, women reported elevated mental health problems compared to men. Women also reported increased COVID-19-relevant stressors, including less access to sufficient personal protective equipment and less support from colleagues than men; however, men reported increased contact with COVID-19 patients. At the country-level, HCWs working in countries with higher gender inequality reported lower levels of mental health problems. Higher COVID-19 mortality rates were associated with increased psychological distress among women but not among men. Conclusion Our findings suggest that among HCWs, women may have been disproportionately exposed to several COVID-19-relevant stressors at the individual and country-level. This highlights the importance of considering gender in emergency response efforts to protect women's well-being and ensure adequate healthcare system preparedness during future public health crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".